Managing Datasets and Models - Couverture souple

Campesato, Oswald

Couverture souple

ISBN 10 : 1683929527 ISBN 13 : 9781683929529

Editeur : Mercury Learning & Information, 2023

Synopsis

This book contains a fast-paced introduction to data-related tasks in preparation for training models on datasets. It presents a step-by-step, Python-based code sample that uses the kNN algorithm to manage a model on a dataset.

Chapter One begins with an introduction to datasets and issues that can arise, followed by Chapter Two on outliers and anomaly detection. The next chapter explores ways for handling missing data and invalid data, and Chapter Four demonstrates how to train models with classification algorithms. Chapter 5 introduces visualization toolkits, such as Sweetviz, Skimpy, Matplotlib, and Seaborn, along with some simple Python-based code samples that render charts and graphs. An appendix includes some basics on using awk. Companion files with code, datasets, and figures are available for downloading.

Features:

• Covers extensive topics related to cleaning datasets and working with models
• Includes Python-based code samples and a separate chapter on Matplotlib and Seaborn
• Features companion files with source code, datasets, and figures from the book

A propos du livre

À propos de l?auteur

Oswald Campesato specializes in Deep Learning, Python, Data Science, and generative AI. He is the author/co-author of over forty-five books including Google Gemini for Python, Large Language Models, and GPT-4 for Developers (all Mercury Learning).

Détails de l'édition

• Éditeur: Mercury Learning & Information
• Date d'édition: 2023
• Langue: anglais
• ISBN 10: 1683929527
• ISBN 13: 9781683929529
• Reliure: Broché
• Nombre de pages: 368
• Coordonnées du fabricant: non disponible
• Personne responsable: non disponible